Randomized algorithms
Size-estimation framework with applications to transitive closure and reachability
Journal of Computer and System Sciences
Maintaining Biconnected Components of Dynamic Planar Graphs
ICALP '91 Proceedings of the 18th International Colloquium on Automata, Languages and Programming
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Seeking stable clusters in the blogosphere
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
EnBlogue: emergent topic detection in web 2.0 streams
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Clustering memes in social media
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Efficient processing of streaming graphs for evolution-aware clustering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Due to their real time nature, microblog streams are a rich source of dynamic information, for example, about emerging events. Existing techniques for discovering such events from a microblog stream in real time (such as Twitter trending topics), have several lacunae when used for discovering emerging events; extant graph based event detection techniques are not practical in microblog settings due to their complexity; and conventional techniques, which have been developed for blogs, web-pages, etc., involving the use of keyword search, are only useful for finding information about known events. Hence, in this paper, we present techniques to discover events that are unraveling in microblog message streams in real time so that such events can be reported as soon as they occur. We model the problem as discovering dense clusters in highly dynamic graphs. Despite many recent advances in graph analysis, ours is the first technique to identify dense clusters in massive and highly dynamic graphs in real time. Given the characteristics of microblog streams, in order to find clusters without missing any events, we propose and exploit a novel graph property which we call short-cycle property. Our algorithms find these clusters efficiently in spite of rapid changes to the microblog streams. Further we present a novel ranking function to identify the important events. Besides proving the correctness of our algorithms we show their practical utility by evaluating them using real world microblog data. These demonstrate our technique's ability to discover, with high precision and recall, emerging events in high intensity data streams in real time. Many recent web applications create data which can be represented as massive dynamic graphs. Our technique can be easily extended to discover, in real time, interesting patterns in such graphs.